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AI Driven InsurerEmerging Risk & Opportunity in a Digital WorldA View from the Startup, DataRobot Under the

Spotlight by Satadru Sengupta and Sophie Roberts

Dubai World Insurance Congress | February 2018

Satadru SenguptaGM Insurance, DataRobot Inc.

Confidential | Copyright © DataRobot, Inc. | All Rights Reserved

The world’s most advanced Enterprise Machine Learning Automation platform

2012Founded, HQ in Boston, MA

$124MIn funding

400,000,000+Models built on DataRobot Cloud

180+Data Scientists & Engineers (of 300+)

4#1 ranked Data Scientists

50+Top 3 finishes

INSURANCE FINTECH HEALTHCARE MARKETING BANKING MANY MORE

… last 4 weeks

Fresh Capital From Big PlayersBlurred line between Silicon Valley & Wall St.

Trickle down effect on medium and small players

"New technology is transforming the way we work, and it is allowing the competition to do

better than what we can.

The strange thing is we know the urgency, and yet there is inertia."

Inga Beale, The Lloyds of London, DWIC 2017

DIGITAL TRANSFORMATION

1. Elastic Cloud Computing (Cloud)

2. Internet of Things (IoT)

3. Artificial Intelligence (AI)

Elements of Digital Transformation


DATA

is the new oil


AI & Machine Learning

monetizes the data

Transform

OR

Disappear

“For us, Technology is not a cost center, it is the Company”

Daniel Schreiber, CEO, Lemonade

Decision making in the 21st-century insurance

companies

“Whatever else it produces, an organization is a factory that produces judgments and decisions.”

Daniel Kahneman, Thinking Fast & Slow

Machine Learning

Deep Learning

Better Decision, Faster Decision.

Machine doesn’t fall for biases.

Augmentation as opposed to “full autonomy”

Machine Learning

Deep Learning

AI: computer systems able to perform tasks that ordinarily require human intelligence

Machine Learning

Deep Learning

AI: computer systems able to perform tasks that ordinarily require human intelligence

Powered by boring stuff

Powered by deep learning

Powered by ML (non-DL)

Confidential. Copyright © DataRobot, Inc. - All Rights Reserved

How Machine Learning Is Used In Insurance

Historical Examples to Learn From

Machine Learning

Algorithm+ =

New

Dat

a Good Risk

Poor Risk

Average Risk

What are the risks an insurance company cares about?● Marketing & Distribution: acquisition, retention (short-term policies, e.g. as in P&C), lapse

(long-term policies, e.g. in life), submission prioritization, product recommendation

● Underwriting: underwriting risk (# claims, $ severity, mortality, health), agent mis-conduct, risk factors mis-reporting (roof age, insured age, smoking status, mileage, diabetes, family history)

● Claims: fraud, litigation, subro/ recovery, severity, duration

● Others: audit, IoT, loss prevention


P&C Use Case: Claims Management● Manage claims proactively to avoid fraud, contain severity of claims, and reduce operational

cost

● Predict the claim outcome based on the information available at the First Notice of Loss (FNOL) and accordingly triage the claim to the right adjustor and/ or SIU

○ Predict the likelihood of claim being a fraud claim and assign high risk claims it to SIU for manual review

○ Predict complexity of the claim (if complex and long duration then propose settlement and assign to specialized adjustor):

■ Size of the claim■ Duration of the claim■ Likelihood of litigation

○ Predict likelihood of subrogation opportunity

● How we operationalize? Claims modeling requires transparency that will help claim adjustors and fraud investigator understand what are the drivers of the predictive model and predictions.

● Economic value: increased operational efficiency, loss avoidance, loss containment


Insurance

Being AI-Driven

Why Automated Machine Learning is the right strategy

Confidential. Copyright © DataRobot, Inc. and Cloudera, Inc. - All Rights Reserved

PREREQUISITES

1. Knowledge of the overall & specific problem2. Knowledge of the data3. Ability to write code to gather data4. Ability to write code to explore/inspect data5. Ability to write code to manipulate data6. Ability to write code to extract actionable intel7. Ability to write code to build models8. Ability to write code to implement models 9. Foundational statistics

10. Internals of algorithms 11. Practical knowledge and experience12. Knowing how to interpret and explain models

Requirement without AI & Robotic Process Automation

Math &Stats

DomainExpertise

ProgrammingSkills


Automated Machine Learning

AI (artificial intelligence) builds robotic

process automation for predictive

analytics - that focuses on accuracy,

transparency, and ease of use.


PREREQUISITES

1. Knowledge of the overall & specific problem2. Knowledge of the data

Requirement with AI & Robotic Process Automation

Math &Stats

DomainExpertise

ProgrammingSkills

AUTOMATED


Predictive Analytics Challenge

Data Scientists

Domain Expertise

Your Employees


The “Failed” Approach: Data Scientist Focused

Data Scientists

Domain Expertise

Your Employees

https://hbr.org/2016/12/why-youre-not-getting-value-from-your-data-science

https://hbr.org/2016/12/why-youre-not-getting-value-from-your-data-science


Large Scale Delivery Model: Process Automation Focused

Data Scientists

Domain Expertise

Your Employees

MIT Tech Review Article

https://www.technologyreview.com/s/608921/you-could-become-an-ai-master-before-you-know-it-heres-how/


Accuracy

One or Two Algorithms.


Transparency

Only Selected Few Can Explain

Anyone Can Explain


Ease of Use

Artisanal: Only Selected Few Can Use

Anyone Can Use

THANK YOU

[email protected]/insurance

VISIT US AT THE BOOTH TO SEE HOW AUTOMATED MACHINE LEARNING WORKS

mailto:[email protected]

http://www.datarobot.com/insurance

Documents

AI Driven Insurer - Global Reinsuranceevents.globalreinsurance.com/dwic/wp-content/uploads/... · 2018-07-02 · AI Driven Insurer Emerging Risk & Opportunity in a Digital World A